GTM Engineer
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Job Description
GTM Engineer designing automated systems at AI company focusing on revenue growth. Collaborates across teams to enhance GTM strategies and implement signal-driven actions.
Responsibilities:
- Build GTM Signal Infrastructure – Design automated systems that score ICP fit, map buying committees, support visibility into buying group engagement and coverage, and track multi-threaded engagement across target accounts.
- Capture Intent & Engagement at Scale – Aggregate and normalize intent, website, content, ad, and event signals to understand real buying behavior by account and persona.
- Detect Buying Stage & Deal Health – Translate multi-signal engagement patterns into buying-stage readiness, momentum shifts, and stalled-deal risk indicators.
- Orchestrate Automated GTM Actions – Trigger real-time outreach, personalization, audience syncing, nurtures, and deal-acceleration plays based on live engagement signals.
- Build Internal Tooling & Agent Workflows – Ship AI agents, integrations, and automation tooling that turn manual GTM workflows into scalable revenue systems.
- Partner With Marketing, Sales, and RevOps – Translate GTM strategy into scalable systems and automation.
- Add Data Quality & Governance – Maintain data reliability, normalization, and signal accuracy across GTM systems
Requirements:
- New grad or early-career engineer who has shipped real production code (internships, co-ops, side projects all count)
- Alternatively, a marketer with strong technical skills who uses automation and AI to engineer scalable, signal-driven growth systems
- Strong scripting and automation comfort level (Python and/or TypeScript preferred)
- Experience working with APIs, data pipelines, and integration-heavy systems
- Builder mindset: you see a problem, you fix it, you ship it
- High ownership, high urgency, low ego
- Product instincts: you can think through user needs and design workflows that make sense
- Interest in GTM and how companies actually scale revenue
Benefits:
- Work directly on problems tied to revenue and real-world adoption
- Ship constantly and see impact immediately
- Operate like a founder: high trust, high ownership, high velocity
- Build the tooling layer that scales an AI company
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